IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v37y2026i1p196-220.html

Unleashing China's coal conservation potentials by analyzing efficiency of energy intensive industries: A Logarithm Mean Divisia Index (LMDI) model

Author

Listed:
  • Zulqarnain Mushtaq
  • Wei Wei
  • Jie Liu

Abstract

Considering China's ambitions for carbon peaking as of 2030 to ensure environmental protection and energy security, the present study is intended to explore sustainable pathways to reduce coal consumption by enhancing energy efficiency. The current article estimates coal consumption efficiency and radial super-efficiency by applying DEA-CCR and radial super-efficiency models. In the second stage, the Logarithm Mean Divisia Index (LMDI) and DEA-Malmquist models were used to explore the components of coal consumption in China's six key energy-intensive industries from 2000 to 2020. Findings indicate that (1) there is a substantial coal consumption efficiency gap among these industries, and they are working well below the production frontier. (2) Findings of DEA-Malmquist indicate that technological changes positively contributed to total productivity changes, while technical efficiency negatively impacted coal consumption growth. (3) The results of the LMDI model reveal that industrial output growth and structural changes are the key factors accelerating coal consumption. In contrast, the coal intensity had deaccelerated the coal consumption in the energy intensive industries. The current study provides several policy proposals to enhance coal conservation and consumption efficiency to achieve the aspiring goals of sustainable development.

Suggested Citation

  • Zulqarnain Mushtaq & Wei Wei & Jie Liu, 2026. "Unleashing China's coal conservation potentials by analyzing efficiency of energy intensive industries: A Logarithm Mean Divisia Index (LMDI) model," Energy & Environment, , vol. 37(1), pages 196-220, February.
  • Handle: RePEc:sae:engenv:v:37:y:2026:i:1:p:196-220
    DOI: 10.1177/0958305X241238328
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X241238328
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0958305X241238328?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Li, Mengna & Pan, Xiongfeng & Yuan, Sai, 2022. "Do the national industrial relocation demonstration zones have higher regional energy efficiency?," Applied Energy, Elsevier, vol. 306(PA).
    3. Qi, Xiaoyan & Guo, Pibin & Guo, Yanshan & Liu, Xiuli & Zhou, Xijun, 2020. "Understanding energy efficiency and its drivers: An empirical analysis of China’s 14 coal intensive industries," Energy, Elsevier, vol. 190(C).
    4. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    5. Ke, Jing & Price, Lynn & Ohshita, Stephanie & Fridley, David & Khanna, Nina Zheng & Zhou, Nan & Levine, Mark, 2012. "China's industrial energy consumption trends and impacts of the Top-1000 Enterprises Energy-Saving Program and the Ten Key Energy-Saving Projects," Energy Policy, Elsevier, vol. 50(C), pages 562-569.
    6. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    7. Chong, ChinHao & Ma, Linwei & Li, Zheng & Ni, Weidou & Song, Shizhong, 2015. "Logarithmic mean Divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows," Energy, Elsevier, vol. 85(C), pages 366-378.
    8. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    9. Jie Wu & Qingyuan Zhu & Pengzhen Yin & Malin Song, 2017. "Measuring energy and environmental performance for regions in China by using DEA-based Malmquist indices," Operational Research, Springer, vol. 17(3), pages 715-735, October.
    10. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    11. Lin, Boqiang & Liu, Xia, 2012. "Dilemma between economic development and energy conservation: Energy rebound effect in China," Energy, Elsevier, vol. 45(1), pages 867-873.
    12. Ouyang, Xiaoling & Gao, Beiying & Du, Kerui & Du, Gang, 2018. "Industrial sectors' energy rebound effect: An empirical study of Yangtze River Delta urban agglomeration," Energy, Elsevier, vol. 145(C), pages 408-416.
    13. Wei, Wei & Mushtaq, Zulqarnain & Sharif, Maimoona & Zeng, Xiaowu & Wan-Li, Zhang & Qaisrani, Mumtaz A., 2020. "Evaluating the coal rebound effect in energy intensive industries of China," Energy, Elsevier, vol. 207(C).
    14. Lin, Boqiang & Du, Kerui, 2015. "Measuring energy rebound effect in the Chinese economy: An economic accounting approach," Energy Economics, Elsevier, vol. 50(C), pages 96-104.
    15. Chai, Jian & Du, Mengfan & Liang, Ting & Sun, Xiaojie Christine & Yu, Ji & Zhang, Zhe George, 2019. "Coal consumption in China: How to bend down the curve?," Energy Economics, Elsevier, vol. 80(C), pages 38-47.
    16. Liu, Na & Ang, B.W., 2007. "Factors shaping aggregate energy intensity trend for industry: Energy intensity versus product mix," Energy Economics, Elsevier, vol. 29(4), pages 609-635, July.
    17. Du, Kerui & Xie, Chunping & Ouyang, Xiaoling, 2017. "A comparison of carbon dioxide (CO2) emission trends among provinces in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 19-25.
    18. Fromme, JW, 1996. "Energy conservation in the Russian manufacturing industry. Potentials and obstacles," Energy Policy, Elsevier, vol. 24(3), pages 245-252, March.
    19. Fare, R. & Grosskopf, S. & Roos, P., 1995. "Productivity and quality changes in Swedish pharmacies," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 137-144, April.
    20. Wu, Haitao & Hao, Yu & Ren, Siyu, 2020. "How do environmental regulation and environmental decentralization affect green total factor energy efficiency: Evidence from China," Energy Economics, Elsevier, vol. 91(C).
    21. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    22. Orea, Luis & Llorca, Manuel & Filippini, Massimo, 2015. "A new approach to measuring the rebound effect associated to energy efficiency improvements: An application to the US residential energy demand," Energy Economics, Elsevier, vol. 49(C), pages 599-609.
    23. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    24. Geller, Howard & Harrington, Philip & Rosenfeld, Arthur H. & Tanishima, Satoshi & Unander, Fridtjof, 2006. "Polices for increasing energy efficiency: Thirty years of experience in OECD countries," Energy Policy, Elsevier, vol. 34(5), pages 556-573, March.
    25. Alsaleh, Mohd & Abdul-Rahim, A.S. & Mohd-Shahwahid, H.O., 2017. "Determinants of technical efficiency in the bioenergy industry in the EU28 region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1331-1349.
    26. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    27. Mushtaq, Zulqarnain & Wei, Wei & Jamil, Ihsan & Sharif, Maimoona & Chandio, Abbas Ali & Ahmad, Fayyaz, 2022. "Evaluating the factors of coal consumption inefficiency in energy intensive industries of China: An epsilon-based measure model," Resources Policy, Elsevier, vol. 78(C).
    28. Zhang, Yue-Jun & Peng, Hua-Rong & Su, Bin, 2017. "Energy rebound effect in China's Industry: An aggregate and disaggregate analysis," Energy Economics, Elsevier, vol. 61(C), pages 199-208.
    29. Ouyang, Xiaoling & Chen, Jiaqi & Du, Kerui, 2021. "Energy efficiency performance of the industrial sector: From the perspective of technological gap in different regions in China," Energy, Elsevier, vol. 214(C).
    30. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    31. Alsaleh, Mohd & Abdul-Rahim, A.S., 2018. "Determinants of cost efficiency of bioenergy industry: Evidence from EU28 countries," Renewable Energy, Elsevier, vol. 127(C), pages 746-762.
    32. Mohd Alsaleh & A. S. Abdul-Rahim, 2019. "Bioenergy Intensity and Its Determinants in European Continental Countries: Evidence Using GMM Estimation," Resources, MDPI, vol. 8(1), pages 1-14, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Wei & Mushtaq, Zulqarnain & Sharif, Maimoona & Zeng, Xiaowu & Wan-Li, Zhang & Qaisrani, Mumtaz A., 2020. "Evaluating the coal rebound effect in energy intensive industries of China," Energy, Elsevier, vol. 207(C).
    2. Jin, Taeyoung & Kim, Jinsoo, 2019. "A new approach for assessing the macroeconomic growth energy rebound effect," Applied Energy, Elsevier, vol. 239(C), pages 192-200.
    3. Jafari, Mahboubeh & Stern, David I. & Bruns, Stephan B., 2022. "How large is the economy-wide rebound effect in middle income countries? Evidence from Iran," Ecological Economics, Elsevier, vol. 193(C).
    4. Chong, Chin Hao & Zhou, Xiaoyong & Zhang, Yongchuang & Ma, Linwei & Bhutta, Muhammad Shoaib & Li, Zheng & Ni, Weidou, 2023. "LMDI decomposition of coal consumption in China based on the energy allocation diagram of coal flows: An update for 2005–2020 with improved sectoral resolutions," Energy, Elsevier, vol. 285(C).
    5. Yan, Zheming & Ouyang, Xiaoling & Du, Kerui, 2019. "Economy-wide estimates of energy rebound effect: Evidence from China's provinces," Energy Economics, Elsevier, vol. 83(C), pages 389-401.
    6. Qiu, Ziang & Zhang, Xibin & Zhang, Yang, 2025. "Empowering energy security: The impact of geopolitical risks on green total factor energy efficiency," Energy Economics, Elsevier, vol. 151(C).
    7. Mushtaq, Zulqarnain & Wei, Wei & Jamil, Ihsan & Sharif, Maimoona & Chandio, Abbas Ali & Ahmad, Fayyaz, 2022. "Evaluating the factors of coal consumption inefficiency in energy intensive industries of China: An epsilon-based measure model," Resources Policy, Elsevier, vol. 78(C).
    8. Ruijing Zheng & Yu Cheng & Haimeng Liu & Wei Chen & Xiaodong Chen & Yaping Wang, 2022. "The Spatiotemporal Distribution and Drivers of Urban Carbon Emission Efficiency: The Role of Technological Innovation," IJERPH, MDPI, vol. 19(15), pages 1-22, July.
    9. Forsund, Finn R. & Sarafoglou, Nikias, 2005. "The tale of two research communities: The diffusion of research on productive efficiency," International Journal of Production Economics, Elsevier, vol. 98(1), pages 17-40, October.
    10. Hokey Min & Young‐Hyo Ahn & Jin‐Hee Ma, 2024. "Measuring dynamic supply chain risks for the offshoring decision in the post‐COVID‐19 era: A longitudinal study," Transportation Journal, John Wiley & Sons, vol. 63(3), pages 188-206, July.
    11. Lin, Boqiang & Raza, Muhammad Yousaf, 2021. "Analysis of electricity consumption in Pakistan using index decomposition and decoupling approach," Energy, Elsevier, vol. 214(C).
    12. Xu, Mengmeng & Chen, Can & Zhou, Xiaoshi, 2024. "Enhancing understanding of rebound effect: A novel varying coefficient model for China's industrial sector," Energy, Elsevier, vol. 313(C).
    13. Mai, Nhat Chi, 2015. "Efficiency of the banking system in Vietnam under financial liberalization," OSF Preprints qsf6d, Center for Open Science.
    14. Haian Yu & Zufeng Shang & Fenglai Wang, 2024. "Analysis of the Current Situation of the Construction Industry in Saudi Arabia and the Factors Affecting It: An Empirical Study," Sustainability, MDPI, vol. 16(16), pages 1-23, August.
    15. Thanh Ngo & David Tripe & Duc Khuong Nguyen, 2024. "Estimating the productivity of US agriculture: The Fisher total factor productivity index for time series data with unknown prices," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 68(3), pages 701-712, July.
    16. Rafael Benítez & Vicente Coll-Serrano & Vicente J. Bolós, 2021. "deaR-Shiny: An Interactive Web App for Data Envelopment Analysis," Sustainability, MDPI, vol. 13(12), pages 1-19, June.
    17. Reuben Elan & Verma Bharat Bhushan & Bhat Ramesh, 2001. "Hospital Efficiency: An Empirical Analysis of District and Grant-in-Aid Hospitals in Gujarat," IIMA Working Papers WP2001-07-05, Indian Institute of Management Ahmedabad, Research and Publication Department.
    18. Fan, Di & Peng, Bo & Wu, Jianxin & Zhang, ZhongXiang, 2024. "The convergence of total-factor energy efficiency across Chinese cities: A distribution dynamics approach," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 406-416.
    19. Mariano, Enzo Barberio & Sobreiro, Vinicius Amorim & Rebelatto, Daisy Aparecida do Nascimento, 2015. "Human development and data envelopment analysis: A structured literature review," Omega, Elsevier, vol. 54(C), pages 33-49.
    20. Ricardo Casonatto & Tales Souza & Gustavo Silva & Victor Oliveira & Simone Monteiro, 2025. "Assessing Resource Management in Higher Education Sustainability Projects: A Bootstrap Dea Case Study," Sustainability, MDPI, vol. 17(19), pages 1-16, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:engenv:v:37:y:2026:i:1:p:196-220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.